---
title: FLUX
metatags:
    description: "Deploy FLUX diffusion models with SGLang - 12B/32B rectified flow transformers for high-quality text-to-image generation."
---

import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx';
import { FluxDeployment } from '/src/snippets/diffusion/flux-deployment.jsx';

<DiffusionModelTags tags={["image", "text-to-image", "image editing", "multi-reference"]} />

## 1. Model Introduction

[FLUX](https://blackforestlabs.ai/) is a family of rectified flow transformer models developed by Black Forest Labs for high-quality image generation from text descriptions.

[FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.

**Key Features:**

- **Cutting-edge Output Quality**: Second only to the state-of-the-art FLUX.1 [pro] model
- **Competitive Prompt Following**: Matches the performance of closed-source alternatives
- **Guidance Distillation**: Trained using guidance distillation for improved efficiency
- **Open Weights**: Available for personal, scientific, and commercial purposes under the FLUX [dev] Non-Commercial License

[FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev) is a 32 billion parameter rectified flow transformer capable of generating, editing, and combining images based on text instructions.

**Key Features:**

- **State-of-the-art Performance**: Leading open model in text-to-image generation, single-reference editing, and multi-reference editing
- **No Finetuning Required**: Character, object, and style reference without additional training in one model
- **Guidance Distillation**: Trained using guidance distillation for improved efficiency
- **Open Weights**: Available for personal, scientific, and commercial purposes under the FLUX [dev] Non-Commercial License

For more details, please refer to the [FLUX.1-dev HuggingFace page](https://huggingface.co/black-forest-labs/FLUX.1-dev), [FLUX.2-dev HuggingFace page](https://huggingface.co/black-forest-labs/FLUX.2-dev), and the [official blog post](https://blackforestlabs.ai/announcing-black-forest-labs/).

## 2. SGLang-diffusion Installation

SGLang-diffusion offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

Please refer to the [official SGLang-diffusion installation guide](https://docs.sglang.io/docs/sglang-diffusion/installation) for installation instructions.

## 3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

### 3.1 Basic Configuration

FLUX models are optimized for high-quality image generation. The recommended launch configurations vary by hardware and model version.

**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform and model version. SGLang supports serving FLUX on NVIDIA B200, H200, H100, and AMD MI355X, MI325X, MI300X GPUs and Ascend A2, A3 NPUs.

<FluxDeployment />

### 3.2 Configuration Tips

Currently supported optimizations are listed [here](/docs/sglang-diffusion/compatibility_matrix).

- `--vae-path`: Path to a custom VAE model or HuggingFace model ID (e.g., fal/FLUX.2-Tiny-AutoEncoder). If not specified, the VAE will be loaded from the main model path.
- `--num-gpus`: Number of GPUs to use
- `--tp-size`: Tensor parallelism size (only for the encoder; should not be larger than 1 if text encoder offload is enabled, as layer-wise offload plus prefetch is faster)
- `--sp-degree`: Sequence parallelism size (typically should match the number of GPUs)
- `--ulysses-degree`: The degree of DeepSpeed-Ulysses-style SP in USP
- `--ring-degree`: The degree of ring attention-style SP in USP

## 4. API Usage

For complete API documentation, please refer to the [official API usage guide](/docs/sglang-diffusion/api/openai_api).

### 4.1 Generate an Image

```python Example
import base64
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:3000/v1")

response = client.images.generate(
    model="black-forest-labs/FLUX.1-dev",
    prompt="A cat holding a sign that says hello world",
    size="1024x1024",
    n=1,
    response_format="b64_json",
)

# Save the generated image
image_bytes = base64.b64decode(response.data[0].b64_json)
with open("output.png", "wb") as f:
    f.write(image_bytes)
```

### 4.2 Advanced Usage

#### 4.2.1 Cache-DiT Acceleration

SGLang integrates [Cache-DiT](https://github.com/vipshop/cache-dit), a caching acceleration engine for Diffusion Transformers (DiT), to achieve up to 7.4x inference speedup with minimal quality loss. You can set `SGLANG_CACHE_DIT_ENABLED=True` to enable it. For more details, please refer to the SGLang Cache-DiT [documentation](/docs/sglang-diffusion/cache_dit).

**Basic Usage**

```bash Command
SGLANG_CACHE_DIT_ENABLED=true sglang serve --model-path black-forest-labs/FLUX.1-dev
```

**Advanced Usage**

- DBCache Parameters: DBCache controls block-level caching behavior:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Env Variable</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Default</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Fn</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_FN`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Number of first blocks to always compute</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Bn</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_BN`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Number of last blocks to always compute</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>W</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_WARMUP`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Warmup steps before caching starts</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>R</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_RDT`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.24</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Residual difference threshold</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>MC</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_MC`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>3</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Maximum continuous cached steps</td>
    </tr>
  </tbody>
</table>
- TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Env Variable</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Default</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Enable</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_TAYLORSEER`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>false</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Enable TaylorSeer calibrator</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Order</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_TS_ORDER`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Taylor expansion order (1 or 2)</td>
    </tr>
  </tbody>
</table>

  Combined Configuration Example:

```bash Command
SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_FN=2 \
SGLANG_CACHE_DIT_BN=1 \
SGLANG_CACHE_DIT_WARMUP=4 \
SGLANG_CACHE_DIT_RDT=0.4 \
SGLANG_CACHE_DIT_MC=4 \
SGLANG_CACHE_DIT_TAYLORSEER=true \
SGLANG_CACHE_DIT_TS_ORDER=2 \
sglang serve --model-path black-forest-labs/FLUX.1-dev
```

#### 4.2.2 CPU Offload

- `--dit-cpu-offload`: Use CPU offload for DiT inference. Enable if run out of memory.
- `--text-encoder-cpu-offload`: Use CPU offload for text encoder inference.
- `--vae-cpu-offload`: Use CPU offload for VAE.
- `--pin-cpu-memory`: Pin memory for CPU offload. Only added as a temp workaround if it throws "CUDA error: invalid argument".

## 5. Benchmark

### 5.1 Speedup Benchmark

#### 5.1.1 Generate a image

Test Environment:

- Hardware: NVIDIA B200 GPU (1x)
- Model: black-forest-labs/FLUX.1-dev
- sglang diffusion version: 0.5.6.post2

<Tabs>
  <Tab title="NVIDIA B200">
    **Server Command**:

    ```shell Command
    sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000
    ```

    **Benchmark Command**:

    ```shell Command
    python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
        --backend sglang-video --dataset vbench --task t2v --num-prompts 1 --max-concurrency 1
    ```

    **Result**:

    ```text Output
    ================= Serving Benchmark Result =================
    Backend:                                 sglang-image
    Model:                                   black-forest-labs/FLUX.1-dev
    Dataset:                                 vbench
    Task:                                    t2v
    --------------------------------------------------
    Benchmark duration (s):                  50.97
    Request rate:                            inf
    Max request concurrency:                 1
    Successful requests:                     1/1
    --------------------------------------------------
    Request throughput (req/s):              0.02
    Latency Mean (s):                        50.9681
    Latency Median (s):                      50.9681
    Latency P99 (s):                         50.9681
    --------------------------------------------------
    Peak Memory Max (MB):                    27905.19
    Peak Memory Mean (MB):                   27905.19
    Peak Memory Median (MB):                 27905.19
    ============================================================
    ```
  </Tab>
  <Tab title="Ascend A3">
    **Server Command**:

    ```shell Command
    #One A3 card has 2 npu chips
    sglang serve --tp-size 2 --sp-degree 1 --model-path black-forest-labs/FLUX.1-dev --num-gpus 2
    ```

    **Benchmark Command**:

    ```shell Command
    python -m sglang.multimodal_gen.benchmarks.bench_serving --dataset vbench --task text-to-image --num-prompts 1 --max-concurrency 1
    ```

    **Result**:

    ```text Output
    ================= Serving Benchmark Result =================
    Task:                                         text-to-image
    Model:                                        black-forest-labs/FLUX.1-dev
    Dataset:                                      vbench
    --------------------------------------------------
    Benchmark duration (s):                       16.30
    Request rate:                                 inf
    Max request concurrency:                      1
    Successful requests:                          1/1
    Completed outputs:                            1
    Outputs per prompt:                           1
    --------------------------------------------------
    Request throughput (req/s):                   0.06
    Output throughput (outputs/s):                0.06
    Latency Mean (s):                             16.30
    Latency Median (s):                           16.30
    Latency P90 (s):                              16.30
    Latency P95 (s):                              16.30
    Latency P99 (s):                              16.30
    --------------------------------------------------
    Peak Memory Max (MB):                         19972.00
    Peak Memory Mean (MB):                        19972.00
    Peak Memory Median (MB):                      19972.00
    ------------------------------------------------------------
    ```
  </Tab>
</Tabs>

#### 5.1.2 Generate images with high concurrency

<Tabs>
  <Tab title="NVIDIA B200">
    **Server Command** :

    ```shell Command
    sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000
    ```

    **Benchmark Command** :

    ```shell Command
    python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
        --backend sglang-image --dataset vbench --task t2v --num-prompts 20 --max-concurrency 20
    ```

    **Result** :

    ```text Output
    ================= Serving Benchmark Result =================
    Backend:                                 sglang-image
    Model:                                   black-forest-labs/FLUX.1-dev
    Dataset:                                 vbench
    Task:                                    t2v
    --------------------------------------------------
    Benchmark duration (s):                  111.79
    Request rate:                            inf
    Max request concurrency:                 20
    Successful requests:                     20/20
    --------------------------------------------------
    Request throughput (req/s):              0.18
    Latency Mean (s):                        67.0646
    Latency Median (s):                      66.9691
    Latency P99 (s):                         110.8949
    --------------------------------------------------
    Peak Memory Max (MB):                    27917.19
    Peak Memory Mean (MB):                   27916.59
    Peak Memory Median (MB):                 27917.19
    ============================================================
    ```
  </Tab>
  <Tab title="Ascend A3">
    **Server Command** :

    ```shell Command
    #One A3 card has 2 npu chips
    sglang serve --tp-size 2 --sp-degree 1 --model-path black-forest-labs/FLUX.1-dev --num-gpus 2
    ```

    **Benchmark Command** :

    ```shell Command
    python -m sglang.multimodal_gen.benchmarks.bench_serving --dataset vbench --task text-to-image --num-prompts 20 --max-concurrency 20
    ```

    **Result** :

    ```text Output
    ================= Serving Benchmark Result =================
    Task:                                         text-to-image
    Model:                                        black-forest-labs/FLUX.1-dev
    Dataset:                                      vbench
    --------------------------------------------------
    Benchmark duration (s):                       300.85
    Request rate:                                 inf
    Max request concurrency:                      20
    Successful requests:                          18/20
    Completed outputs:                            18
    Outputs per prompt:                           1
    --------------------------------------------------
    Request throughput (req/s):                   0.06
    Output throughput (outputs/s):                0.06
    Latency Mean (s):                             155.16
    Latency Median (s):                           155.11
    Latency P90 (s):                              266.30
    Latency P95 (s):                              280.15
    Latency P99 (s):                              291.23
    --------------------------------------------------
    Peak Memory Max (MB):                         19972.00
    Peak Memory Mean (MB):                        19972.00
    Peak Memory Median (MB):                      19972.00
    ------------------------------------------------------------
    ```
  </Tab>
</Tabs>
